In today's world, security and access control systems for protecting secure or restricted areas, such as airports, government defense facilities, or corporate sites containing confidential proprietary information, are in high demand. To be effective, security systems for these types of areas must first of all be accurate, that is, a security system must accurately determine both objects or persons who are authorized to enter as well as those objects or persons who must not be granted access. Effective security systems for high volume areas, such as airports, must be fast and easy-to-use. Systems that further add to delay at airports or require a traveler to carry something would further inconvenience the traveler and therefore be unacceptable.
For these reasons, many conventional security systems use biometric data, such as fingerprints, retinal eye patterns, or hand geometry, to identify a person. The captured biometric data is compared to a database of biometric data representing known persons. If the acquired data matches the profile data of an individual from the database, the person may be granted access to a secure facility or identified as someone who should not be granted access. These conventional security systems, however, typically require cooperation of a target, such as a person, and therefore are not designed for “non-cooperative” scenarios, wherein biometric data often must be acquired passively, that is, without requiring any special action, effort or participation of the target to be identified.
Biometric identification systems which do not require a target's cooperation are enjoying great popularity and demand among authorities and companies where security is or is becoming important, such as airports, banks, workplaces and other secure or restricted places. For instance, systems employing biometric facial recognition, unlike security systems that require target cooperation (e.g., fingerprint recognition, iris recognition, etc.), require no human cooperation, awareness, or contact, work passively at a distance in real time environment, and can be easily integrated into existing security systems. Consequently, biometric facial recognition systems are suitable for non-intrusive security checks at high-risk public sites (airports, borders, etc.), and for passive identification and monitoring of known criminals and terrorists.
Conventional systems and methods for biometric facial recognition typically use two-dimensional (“2D”) images of a person's face, similar to images received from video or photo cameras. Although 2D image data is easy to collect, it is not uniquely distinctive and the quality of the acquired data is dependent on a variety of conditions, such as ambient light conditions, view angle, etc. Consequently, the reliability of 2D biometric facial recognition systems lags behind many conventional security systems that use biometric data, such as fingerprints, retinal eye patterns, or hand geometry, to identify a person.
Some conventional systems, such as those only capable of capturing 2D image data, experience difficulty in isolating a target image, such as a person's face from other objects, and consequently experience difficulty in identifying the target image. Such systems also experience accuracy problems because the quality of the acquired data is negatively affected by shadows on, the angle of, or movement by the person or object to be identified. In addition, these systems experience difficulties in making assumptions about the target image based on the target's shape because they do not have 3D image data and therefore do not have distance measurements. Furthermore, many of these systems do not reject corrupted image data, further increasing their probability of error.
Other conventional security systems, though capable of capturing 3D image data for target identification, require cooperation of a target for capturing image data and therefore have the same disadvantages as conventional security systems that require target cooperation, e.g., fingerprint recognition, iris recognition, etc. For instance, in conventional 3D image recognition systems, a person may have to knowingly stay in or move through a designated area armed with cameras, such as a portal or doorway, to facilitate image data capture. Some security systems using 3D data do not require cooperation of the target; however, they experience difficulties isolating a target image from a sea of unwanted images, such as trying to isolate one person from a crowd of people in an airport. In addition, speed, accuracy, and portability have been recurrent and difficult to goals to achieve simultaneously for devices that scan, measure or otherwise collect geometric data about 3D objects for identification and recognition.
Still other methods and systems that collect 3D image data are limited by the way the 3D image data is collected. For example, single dot optical scanning systems determines the location of the 3D object based on the angle of reflection of a single point of reflected light. Such systems can digitize only a single point at a time and therefore are relatively slow in collecting a full set of points to describe an object and are further limited by the precision of the movement and positioning of the laser beam. Scan line systems that employ a two-dimensional (“2D”) imager, such as a charge coupled device (“CCD”) camera, for signal detection projects a light plane (i.e., a laser stripe) instead of just one dot and reads the reflection of multiple points depicting the contour of an object at a location that is at a distance from the CCD camera and from which the position can be triangulated. Such systems typically use a bulky, high-precision mechanical system for moving the scanner and are further limited because the laser stripe stays at a fixed angle relative to the camera and the system makes its calculations based on the cylindrical coordinates of its rotating platform. The mathematical simplicity in such a projection system complicates the hardware portion of these devices as they typically depend on the rotational platform mentioned. Also, the simplified geometry does not generally allow for extremely refined reproduction of topologically nontrivial objects, such as objects with holes in them (e.g., a tea pot with a handle). Full realization of triangulation scanning with a non-restrictive geometry has not been achieved in the available devices.
Deficiencies in still other known methods and systems for three-dimensional identification and recognition occur in the matching process. Methods and systems that collect too much data on the acquired person or object or attempt to increase accuracy by increasing the number of attributes compared may suffer performance problems, that is, recognition of the person or object may take too long for practical application. This deficiency must be balanced against collecting too little information or comparing too few features thereby resulting in a system that is so inaccurate as to not be useful.